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The probiotics field, a historically popular yet scientifically debated discipline, is moving beyond a decades-long promotion of 'first-generation' food-derived strains towards the development of 'next-generation probiotics' (NGP) or 'precision probiotics', natural and engineered strains featuring improved human colonization, clinical efficacy and safety profiles. In this Review, we outline the evolution of NGP and means by which their development is designed to tackle challenges of live bacterial therapy related to colonization resistance, in-host evolution, long-term safety and insufficient understanding of therapeutic and off-target mechanisms of activity. We showcase how a variety of emerging strategies enable the identification of NGP strains and define consortia featuring therapeutic potentials in metabolic, immune and oncological diseases. Finally, we discuss how computational and artificial intelligence (AI) advances can reshape NGP development, including AI-based discovery of strains and bioactive compounds; computational-driven design of engineered microorganisms and multi-kingdom consortia; and AI-assisted structural and metabolic network-based modelling predicting personalized NGP function, interactions and therapeutic impacts.

Nitrosamines are potent genotoxic impurities with well reported mutagenic and carcinogenic effects. Their presence has been detected in several pharmaceutical products among them losartan, an angiotensin II receptor blocker. Considering the risks already reported for these impurities, the present study aimed to investigate the influence of four common nitrosamines-N-nitrosodimethylamine (NDMA), N-nitrosodiethylamine (NDEA), N-nitrosodiisopropylamine (NDIPA), and N-nitrosodibutylamine (NDBA)-in the stability profile of losartan API. Quantitative assay was conducted through HPLC and LC-MS, with focus on monitoring degradation rate and degradation products under influence of nitrosamines. Predictive data by in silico investigation employing Zeneth Nexus and Spartan were simultaneously studied, considering degradation products, fragmentation pathway and nitrosamines reactivity. The drug residual content showed variability depending on the nitrosamine evaluated and the stressing condition applied. From photolysis, for example the residual content ranged 80%-90%, with a greater decomposition in samples containing nitrosamines. A greater decomposition was also observed in oxidative degradation, with exception for samples containing NDMA. Acid and basic media caused a significant decomposition, with the residual losartan content in a range of 41%-52% and 28%-51%, respectively. From LC-MS analyzes, these impurities were mostly not detected in the degraded samples, suggesting their consumption during the reaction due to their reactivity. A protective effect from nitrosamines can be also reported possibly due to their reactivity against the stressing factor. Seven degradation products were structurally proposed by LC-MS at m/z 449, m/z 447, m/z 366, m/z 338, m/z 274, m/z 391, and m/z 341, some of them predicted computationally by Zeneth.

To address the uneven spatial distribution and significant variations in observation data quality among multi-GNSS experiment (MGEX) stations, this paper proposes an adaptive station selection method (comprehensive adaptive site selection, CAS) for uncalibrated phase delay (UPD) estimation that incorporates observation data quality, thereby overcoming the limitations of traditional methods that neglect station geometry and data quality. A position dilution of precision (PDOP) and UPD error propagation model is developed. Using marginal benefit theory, the optimal number of stations is determined. A multi-indicator evaluation system based on Dempster-Shafer (D-S) evidence theory is established to assess data quality, enabling a dynamic grid algorithm that balances spatial geometry and data quality. The experiments are conducted using BeiDou‑3 navigation satellite system (BDS‑3) data. Experimental results demonstrate that the proposed method selects 80 optimal stations, accounting for only 30% of the global stations. The estimated Narrow-Lane (NL) UPD products achieve an accuracy better than 0.05 cycles, with a discrepancy of less than 0.002 cycles compared to the full-station solution, indicating comparable precision. Furthermore, the computational time is reduced by 54.1%.

Drug repurposing leverages existing drugs for new indications, accelerating drug development. Computational methods integrating diverse biological and chemical data can systematically prioritize repurposing candidates, but standardized benchmarks for deep learning evaluation are lacking. We present KG-Bench, a GNN benchmarking framework designed to systematically compare the performance of different graph neural network (GNN) architectures on drug-disease association prediction using the Open Targets dataset. We constructed a knowledge graph (KG) of drugs, diseases, and targets, including annotations such as therapeutic area and molecular pathway, and ensured retrospective validation by leveraging regular dataset updates. To avoid data leakage, we removed redundant entities across splits. Benchmarking six GNN architectures, RGCN achieved the highest ranking performance (AUC: 0.91), while TransformerConv showed superior robustness under class imbalance (F1: 0.28 at 1:100 positive: negative ratio), characteristic of real drug repurposing datasets. KG-Bench also assesses bias, node/feature importance, and uses GNNExplainer for interpretability. Our open-source framework enables fair, reproducible evaluation of graph-based drug repurposing algorithms. Data and codes are available at https://github.com/cmbi/Benchmark_GNN_OpenTargets. Supplementary data are available at Bioinformatics online.

The escalating threat of antimicrobial resistance (AMR) has created an urgent need for new antimicrobial agents. Antimicrobial peptides (AMPs) are promising alternatives to conventional antibiotics due to their broad-spectrum activity and reduced risk of resistance development. While most AMP discovery efforts have focused on terrestrial microbes, extreme environments remain largely untapped. Deep-sea hydrothermal vent biofilms, such as those from the Arctic Mid-Ocean Ridges (AMOR), are unique ecosystems characterized by high pressure, temperature gradients, and chemical extremes. These conditions select for microorganisms with specialized adaptations, including the production of bioactive compounds that confer survival advantages. Such peptides may exhibit enhanced stability and novel mechanisms of action, making hydrothermal biofilms an exceptional resource for next-generation antimicrobials. Using metagenomic and metatranscriptomic datasets from nine recently published AMOR biofilms, we predicted 961 AMP sequences with Macrel, of which 873 were unique and showed no identity to entries in the Antimicrobial Peptide Database (APD). AMPs were distributed across 51 microbial phyla, including underrepresented archaeal groups such as Asgardarchaeota, Nanoarchaeota, and Micrarchaeota. Transcriptomic profiling detected AMP expression in 25 phyla, including low-abundance candidate taxa, highlighting active AMP production. In silico minimum inhibitory concentration (MIC) prediction using APEX 1.1 suggested that 16.7% of AMPs may inhibit at least one clinically relevant pathogen, with Acinetobacter baumannii emerging as the most susceptible. Four peptides were synthesized for experimental validation; AMP OLKFNNDA_52_10 exhibited moderate in vitro activity against Staphylococcus aureus and weak activity against Escherichia coli, while showing low cytotoxicity toward human HEK293 cells. Other tested peptides displayed weak or no activity, underscoring discrepancies between computational predictions and biological outcomes. Our study reveals extensive taxonomic and structural diversity of AMPs in Arctic hydrothermal vent biofilms and identifies novel candidates withbioactive potential. These findings emphasize the importance of integrating metagenomics, transcriptomics, machine learning, and experimental validation to uncover bioactive compounds from underexplored microbial ecosystems. Overall, AMOR biofilms represent a rich and untapped source of AMPs, offering new opportunities for antimicrobial drug discovery in the fight against AMR.

Anaplasma phagocytophilum is an obligate intracellular, tick-borne bacterial pathogen capable of causing disease and even mortality in various mammals, including humans. Non-coding RNAs play important regulatory roles in multicellular organisms, including innate and adaptive immune pathways, which control bacterial, parasitic, and viral infections. However, the global transcriptomic landscape encompassing both ncRNAs and mRNAs in HL-60 cells invaded by A. phagocytophilum remains unexplored. Cell apoptosis was evaluated by flow cytometry at multiple time points after HL-60 cell infection with A. phagocytophilum. Total RNA was extracted and analyzed by RNA sequencing (RNA-seq) to delineate expression alterations of long non-coding RNAs (lncRNAs), microRNAs (miRNAs), and messenger RNAs (mRNAs) at 24 h post-infection (hpi). Bioinformatics methods were employed for gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses to elucidate the potential functions of these differentially expressed genes. Furthermore, an integrated bioinformatics approach was applied to systematically construct a competing endogenous RNA (ceRNA) network involving lncRNAs, miRNAs, and mRNAs. A. phagocytophilum infection accelerated HL-60 cell apoptosis at multiple time points, with the most significant effect observed at 24 hpi. Transcriptome profiling at 24 hpi identified substantial differential expression, including 487 lncRNAs, 550 mRNAs, and 22 miRNAs with statistically significant changes in expression. Then, expression patterns of eight lncRNAs, eight mRNAs, and seven miRNAs were experimentally validated through reverse transcription quantitative polymerase chain reaction (RT-qPCR), demonstrating strong correlation with RNA-seq results. Bioinformatics analyses revealed significant enrichment of differentially expressed mRNAs in three key pathways: the PI3K/Akt signaling pathway, the actin cytoskeleton regulation pathway and the p53 signaling pathway. Differentially expressed lncRNAs were largely related to the phospholipase D signaling pathway and pathways related to cortisol and aldosterone synthesis/secretion. The altered miRNAs showed predominant enrichment in Rap1 and NF-κB signaling pathways. Notably, computational reconstruction of the lncRNA-miRNA-mRNA ceRNA network identified hsa-miR-4518 and hsa-miR-3609 as central regulatory nodes. This comprehensive transcriptome study elucidates complex gene regulatory networks activated in HL-60 cells after A. phagocytophilum invasion, with particular emphasis on pathogen-modulated miRNA signatures that coordinate critical pathways governing host immune responses and microbial survival strategies. These findings elucidate previously uncharacterized molecular mechanisms underlying A. phagocytophilum pathogenesis and may provide actionable targets for novel therapeutics.

The juice sac granulation of citrus fruits is a biological disorder that commonly occurs during the stages of growth, mature, and post-harvest, which severely affects the quality and reduces consumer acceptance of fruits. To explore the correlation between granulation and both external morphological characteristics and internal quality characteristics, 11 external and internal quality characteristics of Guanxi honey pomelo were collected and systematically analyzed by principal component analysis and linear regression. Then seven external quality characteristics and one critical characteristics, GR% were applied in machine learning modeling. The results indicated that several characteristics such as single fruit weight, single fruit volume, longitudinal diameter, and transverse diameter showed positive correlations with juice sac granulation rate (GR%), and were subsequently incorporated into classification model development. Among the five models evaluated, support vector machine demonstrated superior performance with a precision and recall rate of 100.00% and 100.00%, respectively, verifying its favorable accuracy and robustness. This research combined traditional statistical approaches with modern computational techniques, offering a reliable screening solution for juice sac granulation degree of Guanxi honey pomelo, which provided potential applicability in citrus processing industries and a theoretical foundation for non-destructive quality assessment.

Digital Twin (DT) technology creates a dynamic virtual representation of a physical system using real-time data and computational modeling. While DTs have demonstrated profound impact in several medical disciplines, their translation into dentistry is still emerging and has not been comprehensively mapped. To systematically review and delineate the current applications, technological advancements, and prospective opportunities of digital twin (DT) technology in dentistry. A scoping review was conducted following the Joanna Briggs Institute (JBI) methodology and reported according to Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) guidelines. A systematic search of MEDLINE (PubMed), EMBASE, Scopus, and Web of Science identified English-language publications from January 2000 to April 2025. All empirical and conceptual studies describing DT development, validation, and/or application in dental contexts were eligible. Two reviewers independently conducted screening and study selection, with a third reviewer resolving discrepancies. No automation tools were used. A total of 5989 records were retrieved, and 7 studies met the inclusion criteria. Included studies represented orthodontics, prosthodontics, endodontics, and dental education. DT applications primarily involved: patient-specific virtual modeling for diagnosis and treatment simulation, predictive or performance-monitoring frameworks using biomechanical/algorithmic analysis, and simulation-based skill training. Most were conceptual or prototype studies with small samples and limited clinical validation. DT technology has substantial potential to enhance precision, simulation, monitoring, and personalization in dentistry. However, current evidence remains constrained by fragmented research, methodological inconsistency and insufficient clinical validation. Future adoption of DT requires standardized data pipelines, robust ethical and regulatory frameworks and interdisciplinary collaboration to achieve clinically meaningful and widely adoptable DT integration in dental care.

Accurate hospital bed occupancy forecasting is essential for effective resource planning and patient flow management. While complex machine learning models have gained popularity in healthcare forecasting, their operational utility often falls short due to high maintenance costs and limited interpretability. This study evaluates the performance and practicality of Prophet, a parsimonious time-series model, for mid-term hospital bed occupancy forecasting. We applied the Prophet model to daily bed occupancy data from the Medical Center - University of Freiburg (2010-2023), incorporating public holidays and a COVID-19 pandemic indicator as exogenous regressors. Prophet decomposes time series into trend, seasonality, and holiday effects, offering interpretable components. Forecast accuracy was assessed via rolling cross-validation over 2022-2023 for horizons of 30, 60, 90, and 180 days. A production-ready forecasting pipeline and dashboard were also implemented using cloud-native tools. Prophet achieved low MAPE values across all horizons (3.21%-3.53%) with coverage above 80%, demonstrating reliable accuracy comparable to or better than more complex models that often require higher computational resources and operational costs, such as deep neural networks. Component analysis revealed patterns aligned with hospital operations; weekly and yearly cycles, and holiday effects, highlighting the model's interpretability. This study shows that mid-term hospital bed occupancy can be accurately forecasted using a simple, interpretable model like Prophet. In contrast to more complex architectures, Prophet offers robust performance with minimal tuning, faster deployment, and clearer insights that are critical in operational settings. These findings reinforce the argument that, for structured forecasting tasks like bed occupancy, simple models can rival complex ones, not only in accuracy, but also in reproducibility, scalability, and operational value.

The aim of this study was to establish body surface area (BSA)-dependent Z-score continuous reference intervals of coronary artery diameter measured by Echocardiography in healthy Chinese children population. Echocardiography measured 1221 healthy Chinese children's coronary artery diameters, including the LMCA, LAD, LCX, RCA-pro, RCA-mid, and RCA-dis. We employed the LMS (Lambda-Mu-Sigma) method for the model and the Haycock equation for the BSA estimation. The coronary artery diameter Z-score continuous reference intervals were established using the LMS method and passed the internal and external validation.  The study outcome provides BSA-dependent Z-score continuous reference intervals for the coronary artery diameters in healthy Chinese children. There is a web Z-score calculator with convenient application for clinicians and ultrasound doctors, facilitating subsequent external validations in a larger population. • Echocardiography is the primary non‑invasive modality for assessing coronary artery (CA) diameter in children, and body surface area (BSA)‑dependent Z‑scores provide a more objective evaluation than absolute values. • The LMS (Lambda‑Mu‑Sigma) method is an internationally accepted approach for establishing continuous reference intervals, as used in the WHO child growth standards. • This study establishes BSA‑dependent Z‑score continuous reference intervals for six coronary artery segments (LMCA, LAD, LCX, RCA‑pro, RCA‑mid, RCA‑dis) specifically in healthy Chinese children aged 0 days to 18 years. • It provides a validated, LMS‑based model along with an online Z‑score calculator and a fully reproducible computational procedure, addressing the lack of updated, ethnicity‑specific continuous reference intervals for the Chinese pediatric population.

As a high-end abrasive material, diamond is prone to defects such as cracks and ablation during laser cutting due to complex physical interactions. Achieving efficient and accurate detection of laser-cut diamond defects is crucial for enhancing product quality and production efficiency. Traditional deep learning object detection algorithms generally face challenges like large parameter magnitudes and heavy computational burdens, making them difficult to apply in potential scenarios of resource-restricted equipment such as handheld inspection devices. In response to this problem, this research develops FAS-YOLO, a lightweight defect detection model for laser-cut diamonds based on the YOLOv11n framework. Initially, the FDConv (Frequency Domain Convolution) module is integrated to reconstruct the C3k2 feature extraction component, enhancing the capture of core defect features.Second, the ADown (Adaptive Downsampling) module is employed to refine the downsampling layer, resolving parameter redundancy. The SEAM attention mechanism is integrated to enhance the detection head module. By learning the importance of different channels and fusing channel information, the model is guided to focus more precisely on defect region features while suppressing background interference such as metal reflections. Experimental results demonstrate that the FAS-YOLO model achieves 92% precision, 80.4% recall, and an mAP50 value of 82.6%. Compared to the baseline architecture YOLOv11n, it achieves competitive performance while reducing the number of parameters, GFLOPS, and model size by 37.4%, 40%, and 34.6%, respectively.

In solid-phase microextraction (SPME) research, selecting a coating adsorbent with good compatibility for target molecules can be difficult, and there is no specific migration testing method for vinyl acetate monomer, which is commonly used in the production of food contact materials (FCMs). First, 13 metal-organic frameworks (MOFs) with different structural characteristics and surface chemical environments were prepared and divided into three groups (good, medium, and poor) based on the dynamic vapor sorption (DVS) method. The distinction of superiority and inferiority determined by the DVS method was completely consistent with that determined by the extraction effect of the SPME probe, and the data from the two variables exhibited a statistically significant positive correlation. Then, the electrostatic potential (ESP) distribution on the typical material surface and target molecule and the charge density difference (CDD) of their interaction during adsorption were obtained using a computational simulation method. The results showed that ZIF-68 and ZIF-70 had the highest adsorption energy, which was consistent with the adsorption performance. Finally, ZIF-68 was selected as the optimal adsorption material, and the extraction conditions were optimized. The optimized method was successfully applied to test the specific migration amounts of several ethylene vinyl acetate (EVA) copolymer materials.

Muscle-invasive bladder cancer (MIBC) presents with variable clinical and pathological features, leading to inconsistent responses to standard treatments such as neoadjuvant chemotherapy (NAC). Although transcriptome profiling has shown differences in NAC response, reliable predictors of treatment outcome remain elusive. Here this study aimed to improve NAC response prediction by integrating multicohort transcriptomic data and spatial protein expression profiles using machine learning, enabling precision diagnostics and therapeutic strategies. Transcriptome analysis from four independent cohorts (n = 399) using diverse gene classifiers revealed molecular features associated with NAC response, particularly genes involved in stress responses, immunity and cell adhesion. The clinical relevance of 74 markers was validated by digital pathology for analyzing spatial protein expression. The machine learning frameworks reduced complex transcriptome and digital pathology datasets to a clinically manageable number of biomarkers, yielding an optimal antibody panel for immunohistochemistry-based clinical diagnostics. Computational pathology-driven predictions of NAC response demonstrated a strong correlation with survival outcomes in patients with MIBC, highlighting their potential clinical utility. Mechanistically, targeting the KEAP1-NRF2 axis suppressed glutathione dynamics, proliferation, stemness features and invasiveness of cisplatin-resistant MIBC cells, thereby resensitizing them to cisplatin. Combination treatment with cisplatin and inhibitors targeting the KEAP1-NRF2 pathway markedly suppressed tumor growth in an orthotopic xenograft model. Therefore, this study integrates machine learning-based transcriptome profiling and digital pathology analysis to refine gene classifiers, provide a personalized and feasible framework for treatment decision-making, and overcome chemoresistance to improve therapeutic efficacy. This study integrates machine learning with transcriptome and digital pathology data to identify and validate predictive biomarkers for neoadjuvant chemotherapy response in muscle-invasive bladder cancer. The optimized biomarkers, along with a proposed antibody combination, may improve precision medicine approaches. The KEAP1-NRF2 pathway was identified as a potential therapeutic target.

The causal roles and interactions of inflammatory cytokines and metabolic reprogramming in serous ovarian carcinoma (SOC) remain unclear. This study explored their relationships using Mendelian randomization (MR). In this two-sample MR analysis, GWAS data of inflammatory cytokines and blood metabolites were used as exposures, and SOC from the FinnGen consortium served as the outcome. Inverse variance weighted (IVW) was the primary MR method, supplemented by MR Egger, weighted median, simple mode, and weighted mode. Two-step mediation MR was applied to evaluate whether specific metabolites mediated the effect of inflammatory cytokines on SOC. Sensitivity analyses, including heterogeneity and pleiotropy tests, were conducted to assess robustness. Five inflammatory cytokines were identified as risk factors for SOC: CSF1 (OR = 1.69, 95% CI: 1.17-2.43), CXCL1 (OR = 1.40, 95% CI: 1.01-1.93), IL-20 (OR = 1.86, 95% CI: 1.03-3.35), IL-8 (OR = 1.61, 95% CI: 1.08-2.39), and VEGF-A (OR = 1.24, 95% CI: 1.00-1.54). Furthermore, 1-Palmitoyl-GPG (16:0) potentially mediates the relationship between IL-8 and SOC, explaining ~10% of the total effect. No pleiotropy or heterogeneity was detected. This two-sample MR study provides preliminary genetic evidence that inflammatory cytokines contribute to SOC risk, with lipid metabolism partially mediating IL-8 effects. These findings highlight the interplay between inflammation and metabolism in SOC pathogenesis and suggest potential biomarkers and therapeutic targets. Due to limited sample sizes and European-only ancestry datasets, these findings require validation in larger, multi-ancestry cohorts.

Advancements in high-throughput sequencing have revolutionized transcriptomics, enabling insights into gene expression, splicing, and fusions. However, RNA-seq analysis remains challenging due to complex splice junctions, multi-mapped reads, and chimeric events. We present DeepSAP, which improves RNA-seq alignment by integrating GSNAP's transcriptome-guided genomic alignment with transformer-based splice-junction scoring. This synergy enhances splice-junction detection, indel identification, and resolution of complex splicing patterns. On the Baruzzo human simulated benchmark across complexities, DeepSAP achieves the highest mean F1-score for splice junction detection, outperforming DRAGEN, novoSplice, STAR, HISAT2, and Subjunc. DeepSAP captures intricate sequence patterns surrounding splice donor and acceptor sites, advancing RNA-seq analysis.

The transition of high-risk neonates from the neonatal intensive care unit (NICU) to home remains a complex process, often hindered by inadequate and fragmented discharge education. This study aimed to develop, implement, and evaluate an interactive telenursing web application designed to enhance discharge education for mothers of high-risk neonates. A multi-method study with a three-phase approach (design, implementation, and evaluation) was conducted to develop and assess a bilingual telenursing web application. The application was developed using the waterfall model. Implementation and evaluation phase in this study was conducted as a quasi-experimental study, involving 60 mothers of high-risk neonates. These mothers were randomly assigned to either an intervention group (N = 30), which received interactive, multimedia-based discharge education via the web application, or a control group (N = 30) that received standard discharge education. Maternal knowledge and user satisfaction were the primary outcome measures. The findings are structured into two distinct components: (1) web application design and (2) implementation, and its evaluation. This application was designed as a bilingual (Persian/English) platform. Its various components include the following: A- Login Page (this web application has three user roles: Mother, nurse, and Supervisor (Editor)), B- Home Page, C- Educational Icons Page, D- multiple questions to assess mothers' understanding of the educational content. E- Nurses' Page, F-Messages Page. Evaluation of the Intervention on maternal knowledge showed that the mean knowledge score in the intervention group was significantly higher than in the control group (p = 0.008). User Satisfaction and Usability questionnaire results showed that the overall mean satisfaction score was 3.90 ± 0.77 out of 5. The highest-scoring domain was Information Quality (Mean ± SD = 4.13 ± 0.63), followed by Aesthetics (Mean ± SD = 3.96 ± 0.66) and Functionality (Mean ± SD = 3.90 ± 0.84). The lowest-rated domain was Subjective Quality (Mean ± SD = 3.63 ± 0.73). Conclusion: The findings indicate that the bilingual, competency-driven bilingual telenursing web application is an effective tool for improving maternal knowledge and engagement in post-NICU care. The platform's features, including real-time nurse-mother communication and adaptive learning modalities, facilitate better discharge preparedness and may contribute to reduced emergency healthcare utilization. Future research should explore integration with electronic health record systems to enable personalized and continuous care pathways.

Liberation from prolonged mechanical ventilation is challenging and its outcomes are poor. Patients who failed at least three spontaneous breathing trials, often referred to as prolonged weaning patients, are usually weaned with protocolized programs in specialized weaning units, but there are no standardized strategies to facilitate their ventilator liberation. The objective of this study was to compare the ventilator liberation rate of two common ventilator weaning programs. Tracheostomized patients with ongoing invasive mechanical ventilation for at least 21 day who were admitted to Barlow Respiratory Hospital for ventilator weaning were studied. Patients who passed spontaneous breathing trial on admission were excluded. In a prospective parallel group, non-blinded clinical study, patients were randomized to receive either the Pressure Support Ventilation (PSV) weaning program or the Therapist-Implemented Patient-Specific (TIPS) weaning program. Randomization was performed using a computer algorithm of block design. The primary outcome was ventilator liberation success. The secondary outcomes were hospital length of stay, physical recovery, discharge disposition and mortality. Significant hospital events were also compared between the groups. N = 25 patients were studied in PSV and N = 26 in the TIPS group. Outcomes were reported for all patients. The liberation success rate at 30 days was 37.5% (standard error, SE = 9.9%) in the PSV and 46.2% (SE = 9.8%) in the TIPS group (p = 0.58, odds ratio, OR 1.42, RD 8.7%, 95% confidence interval, CI=-18.6-35.9). The liberation rate at discharge was 44% (SE = 9.9%) in the PSV group and 53.8% (SE = 9.8%) in the TIPS group (p = 0.54, OR:1.48, RD 9.8%, CI=-17.2-37.2%). The inpatient mortality was: PSV = 24% (SE 8.5%) and TIPS = 11.5% (SE 6.3%), p = 0.291, OR 0.413, RD=-12.5%, CI=-33.2-8.3%. We did not find a significant difference between the two ventilator weaning programs in any of our outcomes, but our study describes a very sick patient population. Continued weaning beyond 30 days had improved liberation success. Both weaning paths are equally beneficial for prolonged mechanical ventilation patients who undergo prolonged weaning. The trial was registered retroactively at ClinicalTrials.gov, NCT06976554.

Reports on the prevalence of sarcopenia in patients with autoimmune hepatitis (AIH) are limited, and its association with prognosis remains unclear. Therefore, we evaluated sarcopenia-related factors and their prognostic impact associated with AIH and compared these findings with those of patients with primary biliary cholangitis (PBC). We retrospectively analyzed 161 patients with AIH or PBC who were followed up at our institution and underwent computed tomography (CT) between January 2004 and February 2025. Data on sex, age, comorbidities, sarcopenia-related factors, treatment, cirrhosis, and clinical outcomes were reviewed. Patients with the PBC-AIH overlap syndrome or concomitant malignancy were excluded. Sarcopenia was assessed using the psoas muscle mass index (PMI) on CT. A total of 67 and 94 patients had AIH and PBC, respectively. The two groups showed no significant differences in the proportion of males, 5-year survival, or the prevalence of cirrhosis at diagnosis and other autoimmune diseases. A low PMI was observed in 32.8% and 17.0% of patients with AIH and PBC, respectively, and was significantly more frequent in patients with AIH (p = 0.02). Among patients with AIH, those with a low PMI had significantly poorer survival than those without it. Factors associated with poor prognosis in patients with AIH included a higher model for the end-stage liver disease (MELD) score, low PMI, and the presence of cirrhosis. CT-based assessment of PMI in patients with AIH may provide supplementary prognostic information for risk stratification at diagnosis.

Youth (i.e., child and adolescent) mental health difficulties are a prevalent concern, with anxiety, depression, and disruptive behavior disorders being the most common presentations. Even though psychotherapy is often recommended to help youth and families manage mental health difficulties, recent meta-analyses suggest that youth psychotherapy is only moderately effective, highlighting a need for further improvement and innovation. Emotion dysregulation is a transdiagnostic risk factor across childhood emotional and behavioral disorders, yet despite the important connection between emotion regulation and psychopathology, little research has been conducted on emotion regulation as a potential mechanism of change during psychotherapy. This study will test the biobehavioral regulation of negative emotion as a transdiagnostic mechanism of change in youth psychotherapy using the Modular Approach to Therapy for Children with Anxiety, Depression, Trauma, or Conduct Problems (MATCH). MATCH is a well-researched therapy program for youth that is suitable for testing transdiagnostic mechanisms of treatment response. This protocol describes a two-site randomized controlled trial that aims to recruit 202 youth between the ages of 8 to 15 years with anxiety, depression, and/or disruptive behavior. Participants are randomized to the MATCH intervention condition or a waitlist control condition. Youth and their parent(s) in both conditions complete in-lab assessments and online questionnaires at the start of the study, every 3 months (i.e., quarterly), and at post-test (i.e., following the intervention/waitlist period). Physiological measures of emotion regulation such as heart rate variability and skin conductance are acquired during lab-based tasks. Youth symptoms and emotion regulation are monitored weekly for both conditions. The primary outcome is change in youth symptoms of psychopathology at post-treatment, and whether this change is mediated by change in behavioral and physiological emotion regulation. Secondary outcomes include parental functioning, parenting, family functioning, impairment, and additional measures of youth psychopathology. Findings from the study are expected to enhance the understanding of processes that drive therapeutic change, ultimately leading to better therapy personalization and effectiveness. ClinicalTrials.gov NCT05637320. Prospectively registered on November 15, 2022. https://clinicaltrials.gov/study/NCT05637320.